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Multi-orientation depthwise extraction for stereo image super-resolution

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Abstract

The increasing trend of binocular imaging in recent years has sparked a surge of interest in stereo image super-resolution. While considerable progress has been made in improving model performance, the potential of single-view and cross-view features remains largely unexplored. To address this, we present a novel network that incorporates both intra-view and inter-view feature extraction to enhance stereo image super-resolution. Specially, we design a multi-orientation depthwise extraction module to sufficiently extract various orientation features within a single view. Additionally, a cross focus module is proposed to capture more reliable hierarchical cross-view features. These modules can be integrated together to exploit trustworthier complementary information for HR image reconstruction. Our experimental results showcase the excellent performance of our method, surpassing all previous state-of-the-art methods for stereo image super-resolution.

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The data generated during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (61303093, 61402278) and the Shanghai Natural Science Foundation (19ZR1419100).

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The first draft of the manuscript was written by Xiangyang Fan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Youdong Ding.

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Fan, X., Ye, R., Cai, F. et al. Multi-orientation depthwise extraction for stereo image super-resolution. SIViP 17, 4087–4095 (2023). https://doi.org/10.1007/s11760-023-02640-w

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  • DOI: https://doi.org/10.1007/s11760-023-02640-w

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